Recent work have combined MIL and CNN to train a weakly supervised object detector in an end-to-end way. Bilen and Vedaldi[1]} proposed a two-stream weakly supervised deep detection network (WSDDN) to get classification and detection scores respectively. Based on WSDDN, Tang et al.[2]} proposed an online instance classifier refinement (OICR) approach to refine the rough output of WSDDN, Kantorov et al.[3]} introduced two different kinds of context-aware guidance to improve localization performance of the network. Tang et al.[4]} also proposed a graph-based center cluster method to alleviate the local optimum problem. Some work have also begun to link weak supervision and strong supervision to comprehensively solve the WSOD problem. Zhang et al.[5]} designed a Weakly-supervised to fully-supervised framework (W2F) which mines high-quality pseudo ground truth to train a fully-supervised object detector. Wang et al.[6]} proposed a weakly supervised collaborative learning approach that adopts WSDDN and Faster-RCNN as weakly and strongly supervised sub-network respectively. From the perspective of optimization, Wang et al.[7]} introduce a continuation optimization method into MIL to boost the detection performance. Arun et al.[8]} designed a novel dissimilarity coefficient based WSOD framework which is optimized by minimizing the difference between an annotation agnostic prediction distribution and an annotation aware conditional distribution. Besides, some work combined the tasks of weakly supervised object detection and segmentation into a unified end-to-end learning architecture[9]}, [10]}.
